Neural Network Classification using Cleaned Clusted

Overview

In this exercise, the cluster to which a mask is assigned to is predicted using a NNET approach. Here are the steps:

1. create/load random masks (here we load pre-created random masks)
2. split into train and test
3. train
4. test

Load Noisy Data with Clusters

if(!grepl(pattern = 'maskPrj$', getwd())) setwd('maskPrj')
if(!exists('maskData')) load('NoisyData10-6Cluster.rda')
str(maskData)

List of 9
 $ x         : num [1:924160, 1:361] 11.1 13.2 13.5 16.7 12.2 ...
 $ y         : int [1:924160] 1 2 3 4 5 6 7 8 9 10 ...
 $ class     : int [1:924160] 4 4 4 4 4 4 4 4 4 4 ...
 $ cleanClass: int [1:924160] 4 4 4 4 4 4 4 4 4 4 ...
 $ pos       : num [1:92416, 1:2] 1 2 3 4 5 6 7 8 9 10 ...
 $ loss      :function (y, yhat, method = c("distance", "mismatch"), xy = pos)  
  ..- attr(*, "srcref")= 'srcref' int [1:8] 1 9 10 1 9 1 1 10
  .. ..- attr(*, "srcfile")=Classes 'srcfilecopy', 'srcfile' <environment: 0x13f370320> 
 $ tiltDeg   : num [1:924160] 0.1187 0.0515 -0.1194 0.0436 0.0658 ...
 $ twistDeg  : num [1:924160] 0.1649 -0.1466 0.1714 0.1164 0.0576 ...
 $ phShift   : num [1:924160] 1 1 1 1 1 1 1 1 0 0 ...

Split into train and test

Only the train index is randomly generated:

set.seed(123)
trIdx <- sample.int(dim(maskData$x)[1], .80*dim(maskData$x)[1])

Train a NNET Model

Data is loaded and clean clusters are encoded into the one-hot format:

# Load the keras library
library(keras)
C_train <- to_categorical(maskData$cleanClass[trIdx]-1)
C_test <- to_categorical(maskData$cleanClass[-trIdx]-1)

A model is defined with the number of output classes, and the number of inputs, and one hidden layer that is twice the size of the input. The size of the hidden layer and the number of hidden layers can be changed to find an optimal setting, a process that could be very time comsuming:

levels(factor(maskData$class))

[1] "1" "2" "3" "4" "5" "6"

k = 6
# Build a sequential model with 361 inputs, 712 hidden neurons and k outputs
model <- keras_model_sequential() %>%
  layer_dense(units = 722, activation = "relu", input_shape = c(361)) %>%
  layer_dense(units = k, activation = "softmax")

# Compile the model with categorical crossentropy loss and adam optimizer
model %>% compile(
  loss = "categorical_crossentropy", # -1/N Sum_i{1 tp N}(Sum_j{1:C}(y_ij * log(p_ij))):
  # y_ij is one-hot coding of the class label; p_ij is the association to class j
  optimizer = "adam",
  metrics = c("accuracy")
)

Now the actual training is performed:

# Train the model for 100 epochs with batch size of 32
history <- model %>% fit(
  x = as.matrix(maskData$x[trIdx,]),
  y = C_train,
  epochs = 20,
  batch_size = 32, #default value
  validation_split = 0.2
)

Epoch 1/20

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18484/18484 [==============================] - 17s 922us/step - loss: 0.1917 - accuracy: 0.9370

18484/18484 [==============================] - 20s 1ms/step - loss: 0.1917 - accuracy: 0.9370 - val_loss: 0.1440 - val_accuracy: 0.9440
Epoch 2/20

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18484/18484 [==============================] - 19s 1ms/step - loss: 0.1299 - accuracy: 0.9501

18484/18484 [==============================] - 21s 1ms/step - loss: 0.1299 - accuracy: 0.9501 - val_loss: 0.1284 - val_accuracy: 0.9493
Epoch 3/20

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18484/18484 [==============================] - 17s 928us/step - loss: 0.1245 - accuracy: 0.9521

18484/18484 [==============================] - 19s 1ms/step - loss: 0.1245 - accuracy: 0.9521 - val_loss: 0.1163 - val_accuracy: 0.9531
Epoch 4/20

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18484/18484 [==============================] - 16s 891us/step - loss: 0.1218 - accuracy: 0.9530

18484/18484 [==============================] - 19s 1ms/step - loss: 0.1218 - accuracy: 0.9530 - val_loss: 0.1108 - val_accuracy: 0.9580
Epoch 5/20

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18484/18484 [==============================] - 17s 914us/step - loss: 0.1201 - accuracy: 0.9534

18484/18484 [==============================] - 19s 1ms/step - loss: 0.1201 - accuracy: 0.9534 - val_loss: 0.1169 - val_accuracy: 0.9537
Epoch 6/20

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18484/18484 [==============================] - 16s 883us/step - loss: 0.1190 - accuracy: 0.9540

18484/18484 [==============================] - 18s 997us/step - loss: 0.1190 - accuracy: 0.9540 - val_loss: 0.1322 - val_accuracy: 0.9489
Epoch 7/20

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18484/18484 [==============================] - 19s 1ms/step - loss: 0.1182 - accuracy: 0.9548 - val_loss: 0.1160 - val_accuracy: 0.9543
Epoch 8/20

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18484/18484 [==============================] - 17s 900us/step - loss: 0.1175 - accuracy: 0.9547

18484/18484 [==============================] - 19s 1ms/step - loss: 0.1175 - accuracy: 0.9547 - val_loss: 0.1197 - val_accuracy: 0.9527
Epoch 9/20

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18484/18484 [==============================] - 17s 946us/step - loss: 0.1162 - accuracy: 0.9550

18484/18484 [==============================] - 20s 1ms/step - loss: 0.1162 - accuracy: 0.9550 - val_loss: 0.1043 - val_accuracy: 0.9585
Epoch 10/20

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18484/18484 [==============================] - 19s 1ms/step - loss: 0.1158 - accuracy: 0.9550 - val_loss: 0.1726 - val_accuracy: 0.9389
Epoch 11/20

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18484/18484 [==============================] - 17s 906us/step - loss: 0.1153 - accuracy: 0.9551

18484/18484 [==============================] - 19s 1ms/step - loss: 0.1153 - accuracy: 0.9551 - val_loss: 0.1153 - val_accuracy: 0.9539
Epoch 12/20

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18484/18484 [==============================] - 16s 866us/step - loss: 0.1147 - accuracy: 0.9552

18484/18484 [==============================] - 18s 982us/step - loss: 0.1147 - accuracy: 0.9552 - val_loss: 0.1031 - val_accuracy: 0.9593
Epoch 13/20

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18484/18484 [==============================] - 18s 971us/step - loss: 0.1143 - accuracy: 0.9555 - val_loss: 0.1083 - val_accuracy: 0.9581
Epoch 14/20

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18484/18484 [==============================] - 16s 868us/step - loss: 0.1145 - accuracy: 0.9554

18484/18484 [==============================] - 18s 983us/step - loss: 0.1145 - accuracy: 0.9554 - val_loss: 0.1432 - val_accuracy: 0.9445
Epoch 15/20

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18484/18484 [==============================] - 16s 870us/step - loss: 0.1139 - accuracy: 0.9556

18484/18484 [==============================] - 18s 987us/step - loss: 0.1139 - accuracy: 0.9556 - val_loss: 0.1028 - val_accuracy: 0.9592
Epoch 16/20

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18484/18484 [==============================] - 16s 871us/step - loss: 0.1137 - accuracy: 0.9554

18484/18484 [==============================] - 18s 987us/step - loss: 0.1137 - accuracy: 0.9554 - val_loss: 0.1132 - val_accuracy: 0.9555
Epoch 17/20

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18484/18484 [==============================] - 19s 1ms/step - loss: 0.1136 - accuracy: 0.9556 - val_loss: 0.1631 - val_accuracy: 0.9393
Epoch 18/20

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18484/18484 [==============================] - 17s 894us/step - loss: 0.1141 - accuracy: 0.9556

18484/18484 [==============================] - 19s 1ms/step - loss: 0.1141 - accuracy: 0.9556 - val_loss: 0.1172 - val_accuracy: 0.9548
Epoch 19/20

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18484/18484 [==============================] - 17s 909us/step - loss: 0.1147 - accuracy: 0.9556

18484/18484 [==============================] - 19s 1ms/step - loss: 0.1147 - accuracy: 0.9556 - val_loss: 0.1624 - val_accuracy: 0.9405
Epoch 20/20

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18484/18484 [==============================] - 17s 910us/step - loss: 0.1137 - accuracy: 0.9557

18484/18484 [==============================] - 19s 1ms/step - loss: 0.1137 - accuracy: 0.9557 - val_loss: 0.1135 - val_accuracy: 0.9550

plot(history, smooth = FALSE)

Test the Model

The performance of the model is measured on the test data:

model %>% evaluate(maskData$x[-trIdx,], C_test) -> nnetAccur

   1/5776 [..............................] - ETA: 1:43 - loss: 0.0532 - accuracy: 0.9688
  89/5776 [..............................] - ETA: 3s - loss: 0.0215 - accuracy: 0.9912  
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 316/5776 [>.............................] - ETA: 2s - loss: 0.1132 - accuracy: 0.9553
 432/5776 [=>............................] - ETA: 2s - loss: 0.1187 - accuracy: 0.9541
 548/5776 [=>............................] - ETA: 2s - loss: 0.1177 - accuracy: 0.9543
 665/5776 [==>...........................] - ETA: 2s - loss: 0.1055 - accuracy: 0.9594
 781/5776 [===>..........................] - ETA: 2s - loss: 0.1002 - accuracy: 0.9612
 898/5776 [===>..........................] - ETA: 2s - loss: 0.1135 - accuracy: 0.9553
1014/5776 [====>.........................] - ETA: 2s - loss: 0.1183 - accuracy: 0.9542
1130/5776 [====>.........................] - ETA: 2s - loss: 0.1166 - accuracy: 0.9546
1245/5776 [=====>........................] - ETA: 2s - loss: 0.1096 - accuracy: 0.9575
1361/5776 [======>.......................] - ETA: 1s - loss: 0.1062 - accuracy: 0.9587
1476/5776 [======>.......................] - ETA: 1s - loss: 0.1140 - accuracy: 0.9557
1590/5776 [=======>......................] - ETA: 1s - loss: 0.1178 - accuracy: 0.9548
1705/5776 [=======>......................] - ETA: 1s - loss: 0.1161 - accuracy: 0.9552
1819/5776 [========>.....................] - ETA: 1s - loss: 0.1117 - accuracy: 0.9568
1935/5776 [=========>....................] - ETA: 1s - loss: 0.1088 - accuracy: 0.9578
2052/5776 [=========>....................] - ETA: 1s - loss: 0.1156 - accuracy: 0.9548
2169/5776 [==========>...................] - ETA: 1s - loss: 0.1168 - accuracy: 0.9542
2285/5776 [==========>...................] - ETA: 1s - loss: 0.1164 - accuracy: 0.9545
2400/5776 [===========>..................] - ETA: 1s - loss: 0.1126 - accuracy: 0.9560
2516/5776 [============>.................] - ETA: 1s - loss: 0.1112 - accuracy: 0.9567
2633/5776 [============>.................] - ETA: 1s - loss: 0.1153 - accuracy: 0.9548
2747/5776 [=============>................] - ETA: 1s - loss: 0.1159 - accuracy: 0.9546
2861/5776 [=============>................] - ETA: 1s - loss: 0.1156 - accuracy: 0.9548
2978/5776 [==============>...............] - ETA: 1s - loss: 0.1126 - accuracy: 0.9560
3096/5776 [===============>..............] - ETA: 1s - loss: 0.1113 - accuracy: 0.9566
3213/5776 [===============>..............] - ETA: 1s - loss: 0.1147 - accuracy: 0.9550
3329/5776 [================>.............] - ETA: 1s - loss: 0.1164 - accuracy: 0.9545
3445/5776 [================>.............] - ETA: 1s - loss: 0.1155 - accuracy: 0.9549
3554/5776 [=================>............] - ETA: 0s - loss: 0.1135 - accuracy: 0.9558
3655/5776 [=================>............] - ETA: 0s - loss: 0.1121 - accuracy: 0.9565
3764/5776 [==================>...........] - ETA: 0s - loss: 0.1142 - accuracy: 0.9555
3866/5776 [===================>..........] - ETA: 0s - loss: 0.1147 - accuracy: 0.9552
3970/5776 [===================>..........] - ETA: 0s - loss: 0.1157 - accuracy: 0.9548
4072/5776 [====================>.........] - ETA: 0s - loss: 0.1146 - accuracy: 0.9554
4176/5776 [====================>.........] - ETA: 0s - loss: 0.1123 - accuracy: 0.9563
4286/5776 [=====================>........] - ETA: 0s - loss: 0.1131 - accuracy: 0.9560
4392/5776 [=====================>........] - ETA: 0s - loss: 0.1153 - accuracy: 0.9550
4505/5776 [======================>.......] - ETA: 0s - loss: 0.1163 - accuracy: 0.9545
4617/5776 [======================>.......] - ETA: 0s - loss: 0.1153 - accuracy: 0.9549
4734/5776 [=======================>......] - ETA: 0s - loss: 0.1132 - accuracy: 0.9557
4846/5776 [========================>.....] - ETA: 0s - loss: 0.1130 - accuracy: 0.9558
4960/5776 [========================>.....] - ETA: 0s - loss: 0.1153 - accuracy: 0.9548
5076/5776 [=========================>....] - ETA: 0s - loss: 0.1166 - accuracy: 0.9543
5192/5776 [=========================>....] - ETA: 0s - loss: 0.1158 - accuracy: 0.9546
5234/5776 [==========================>...] - ETA: 0s - loss: 0.1152 - accuracy: 0.9548
5309/5776 [==========================>...] - ETA: 0s - loss: 0.1139 - accuracy: 0.9553
5412/5776 [===========================>..] - ETA: 0s - loss: 0.1139 - accuracy: 0.9555
5518/5776 [===========================>..] - ETA: 0s - loss: 0.1158 - accuracy: 0.9547
5631/5776 [============================>.] - ETA: 0s - loss: 0.1161 - accuracy: 0.9547
5740/5776 [============================>.] - ETA: 0s - loss: 0.1163 - accuracy: 0.9546
5776/5776 [==============================] - 3s 455us/step - loss: 0.1159 - accuracy: 0.9547

5776/5776 [==============================] - 3s 456us/step - loss: 0.1159 - accuracy: 0.9547

nnetAccur

     loss  accuracy 
0.1159482 0.9547427